MStream outperforms scikit-learn algorithms in anomaly detection

https://arxiv.org/abs/2009.08451

by adamnemecek • 6 years ago

5 4 6 years ago

siddhartb_2• 6 years ago

Hi, I am one of the authors of the work. MStream detects anomalies, intrusions, DoS and DDoS attacks in real time and constant memory. It is built on top of MIDAS (https://github.com/Stream-AD/MIDAS/) and works in a multi-aspect data setting i.e., entries having multiple dimensions such as event-log data, multi-attributed graphs etc. MStream is two orders of magnitude faster while achieving higher accuracy on several publicly available datasets.

Github Repository: https://github.com/Stream-AD/MStream

ldn_tech_exec1• 6 years ago

Awesome work. Would you say this is the state of the art for real-time anomaly detection ?

siddhartb_2• 6 years ago

MStream and MIDAS are more accurate than previous baselines for unsupervised anomaly detection. However, there can be scenarios where some labels (ground truth information) are known. In such cases, a semi-supervised algorithm might work better. We are currently working towards building a semi-supervised approach for anomaly detection in real-time.

To the best of my knowledge, MStream and MIDAS are the fastest and detect anomalies in real-time.

voideng• 6 years ago

Auspex Labs is using MIDAS as part of our scoring for risk detection in network flow analysis.

https://www.auspex-labs.com/

MStream outperforms scikit-learn algorithms in anomaly detection

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